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Investigations on Genetic Algorithm Performances in a Parallel Machines Scheduling Environment

Abstract : Objective: The objective is to propose a resolution method to solve the identical parallel machines scheduling problem with non-renewable resources in manufacturing environment to minimize the total completion time. Introduction: Since the consideration of consumable resources becomes one of the strategic competitive tools to ensure companies performance and the stability of their production systems. This study considers a parallel machines scheduling problem with non-renewable resources. Materials and Methods: TA mathematical model is developed in order to find an optimal solution. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a genetic algorithm is proposed and several heuristics are adapted to minimize the total completion time. Results: The simulation results show that the proposed genetic algorithm gives the same results as the mathematical model for small instances (exact solution) and performs the best compared to heuristics for medium and large instances. Conclusion: The scheduling problem in parallel machine environment with consumables resources is studied in this paper. A mathematical model and a metaheuristic are proposed to solve it in order to minimize the total completion time. The simulation results demonstrate the effectiveness of the proposed metaheuristic.
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https://hal-utt.archives-ouvertes.fr/hal-03320807
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 2:40:26 PM
Last modification on : Friday, August 27, 2021 - 3:14:08 PM

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Fayçal Belkaid, Farouk Yalaoui, Zaki Sari. Investigations on Genetic Algorithm Performances in a Parallel Machines Scheduling Environment. The Open Automation and Control Systems Journal, Bentham open, 2017, 9 (Suppl-1, M6), pp.60-74. ⟨10.2174/1874444301709010060⟩. ⟨hal-03320807⟩

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